Console Output

Training and evaluating model for: Washing Machine
Dataset length: 22402 windows


 NILMModel(
  (conv1d): Conv1d(9, 9, kernel_size=(3,), stride=(1,), padding=(1,))
  (lstm): LSTM(9, 256, num_layers=4, batch_first=True, dropout=0.1)
  (dropout): Dropout(p=0.1, inplace=False)
  (relu): ReLU()
  (output_layer): Linear(in_features=256, out_features=1, bias=True)
)
Epoch [1/300], Train Loss: 0.002976
Validation Loss: 0.003134
Epoch [2/300], Train Loss: 0.002489
Validation Loss: 0.002538
Epoch [3/300], Train Loss: 0.002310
Validation Loss: 0.002451
Epoch [4/300], Train Loss: 0.002273
Validation Loss: 0.002325
Epoch [5/300], Train Loss: 0.002194
Validation Loss: 0.002263
Epoch [6/300], Train Loss: 0.002180
Validation Loss: 0.002326
Epoch [7/300], Train Loss: 0.002145
Validation Loss: 0.002182
Epoch [8/300], Train Loss: 0.002099
Validation Loss: 0.002198
Epoch [9/300], Train Loss: 0.002048
Validation Loss: 0.002106
Epoch [10/300], Train Loss: 0.002017
Validation Loss: 0.002197
Epoch [11/300], Train Loss: 0.001982
Validation Loss: 0.002006
Epoch [12/300], Train Loss: 0.002396
Validation Loss: 0.004759
Epoch [13/300], Train Loss: 0.002349
Validation Loss: 0.002179
Epoch [14/300], Train Loss: 0.002074
Validation Loss: 0.002084
Epoch [15/300], Train Loss: 0.001995
Validation Loss: 0.002031
Epoch [16/300], Train Loss: 0.001945
Validation Loss: 0.001989
Epoch [17/300], Train Loss: 0.001880
Validation Loss: 0.002039
Epoch [18/300], Train Loss: 0.001895
Validation Loss: 0.001912
Epoch [19/300], Train Loss: 0.001829
Validation Loss: 0.001870
Epoch [20/300], Train Loss: 0.001816
Validation Loss: 0.001792
Epoch [21/300], Train Loss: 0.001805
Validation Loss: 0.002378
Epoch [22/300], Train Loss: 0.001807
Validation Loss: 0.001717
Epoch [23/300], Train Loss: 0.001723
Validation Loss: 0.001779
Epoch [24/300], Train Loss: 0.001737
Validation Loss: 0.001632
Epoch [25/300], Train Loss: 0.001741
Validation Loss: 0.001770
Epoch [26/300], Train Loss: 0.001645
Validation Loss: 0.001636
Epoch [27/300], Train Loss: 0.001547
Validation Loss: 0.001406
Epoch [28/300], Train Loss: 0.001469
Validation Loss: 0.001386
Epoch [29/300], Train Loss: 0.001513
Validation Loss: 0.001425
Epoch [30/300], Train Loss: 0.001438
Validation Loss: 0.001319
Epoch [31/300], Train Loss: 0.001422
Validation Loss: 0.001245
Epoch [32/300], Train Loss: 0.001305
Validation Loss: 0.001160
Epoch [33/300], Train Loss: 0.001286
Validation Loss: 0.001553
Epoch [34/300], Train Loss: 0.001225
Validation Loss: 0.001141
Epoch [35/300], Train Loss: 0.001287
Validation Loss: 0.001475
Epoch [36/300], Train Loss: 0.001366
Validation Loss: 0.001746
Epoch [37/300], Train Loss: 0.001350
Validation Loss: 0.001491
Epoch [38/300], Train Loss: 0.001219
Validation Loss: 0.000972
Epoch [39/300], Train Loss: 0.001169
Validation Loss: 0.000897
Epoch [40/300], Train Loss: 0.001041
Validation Loss: 0.001575
Epoch [41/300], Train Loss: 0.000999
Validation Loss: 0.001662
Epoch [42/300], Train Loss: 0.001193
Validation Loss: 0.001763
Epoch [43/300], Train Loss: 0.001281
Validation Loss: 0.000913
Epoch [44/300], Train Loss: 0.001273
Validation Loss: 0.001082
Epoch [45/300], Train Loss: 0.001318
Validation Loss: 0.001383
Epoch [46/300], Train Loss: 0.001101
Validation Loss: 0.000972
Epoch [47/300], Train Loss: 0.000862
Validation Loss: 0.000772
Epoch [48/300], Train Loss: 0.000938
Validation Loss: 0.000765
Epoch [49/300], Train Loss: 0.000986
Validation Loss: 0.000853
Epoch [50/300], Train Loss: 0.000792
Validation Loss: 0.000762
Epoch [51/300], Train Loss: 0.000818
Validation Loss: 0.000696
Epoch [52/300], Train Loss: 0.000850
Validation Loss: 0.001005
Epoch [53/300], Train Loss: 0.000782
Validation Loss: 0.000630
Epoch [54/300], Train Loss: 0.000739
Validation Loss: 0.000660
Epoch [55/300], Train Loss: 0.000753
Validation Loss: 0.000534
Epoch [56/300], Train Loss: 0.000748
Validation Loss: 0.000597
Epoch [57/300], Train Loss: 0.000685
Validation Loss: 0.000663
Epoch [58/300], Train Loss: 0.001002
Validation Loss: 0.001156
Epoch [59/300], Train Loss: 0.001087
Validation Loss: 0.000722
Epoch [60/300], Train Loss: 0.000807
Validation Loss: 0.001126
Epoch [61/300], Train Loss: 0.000618
Validation Loss: 0.000580
Epoch [62/300], Train Loss: 0.000529
Validation Loss: 0.000549
Epoch [63/300], Train Loss: 0.000856
Validation Loss: 0.000693
Epoch [64/300], Train Loss: 0.000650
Validation Loss: 0.000627
Epoch [65/300], Train Loss: 0.000602
Validation Loss: 0.000455
Epoch [66/300], Train Loss: 0.000496
Validation Loss: 0.000443
Epoch [67/300], Train Loss: 0.000513
Validation Loss: 0.000426
Epoch [68/300], Train Loss: 0.000516
Validation Loss: 0.000489
Epoch [69/300], Train Loss: 0.000468
Validation Loss: 0.000404
Epoch [70/300], Train Loss: 0.001001
Validation Loss: 0.001275
Epoch [71/300], Train Loss: 0.001365
Validation Loss: 0.001013
Epoch [72/300], Train Loss: 0.000604
Validation Loss: 0.000622
Epoch [73/300], Train Loss: 0.000511
Validation Loss: 0.000411
Epoch [74/300], Train Loss: 0.000543
Validation Loss: 0.000443
Epoch [75/300], Train Loss: 0.000554
Validation Loss: 0.000592
Epoch [76/300], Train Loss: 0.000425
Validation Loss: 0.001014
Epoch [77/300], Train Loss: 0.000484
Validation Loss: 0.000561
Epoch [78/300], Train Loss: 0.000431
Validation Loss: 0.000480
Epoch [79/300], Train Loss: 0.000573
Validation Loss: 0.000384
Epoch [80/300], Train Loss: 0.000447
Validation Loss: 0.000582
Epoch [81/300], Train Loss: 0.000551
Validation Loss: 0.000655
Epoch [82/300], Train Loss: 0.000581
Validation Loss: 0.000383
Epoch [83/300], Train Loss: 0.000394
Validation Loss: 0.000361
Epoch [84/300], Train Loss: 0.000587
Validation Loss: 0.002146
Epoch [85/300], Train Loss: 0.001493
Validation Loss: 0.000867
Epoch [86/300], Train Loss: 0.000664
Validation Loss: 0.000501
Epoch [87/300], Train Loss: 0.000547
Validation Loss: 0.000473
Epoch [88/300], Train Loss: 0.000528
Validation Loss: 0.000574
Epoch [89/300], Train Loss: 0.000622
Validation Loss: 0.000426
Epoch [90/300], Train Loss: 0.000453
Validation Loss: 0.000450
Epoch [91/300], Train Loss: 0.000495
Validation Loss: 0.000398
Epoch [92/300], Train Loss: 0.000434
Validation Loss: 0.000393
Epoch [93/300], Train Loss: 0.000412
Validation Loss: 0.000408
Early stopping triggered

Evaluating model for: Washing Machine
Validation MAE: 10.598794 W
Validation MSE: 3034.895264 W²
Validation RMSE: 55.089882 W
Signal Aggregate Error (SAE): 0.207103
Normalized Disaggregation Error (NDE): 0.302644

Training and Validation Loss

Training Loss Plot

Interactive Plot